• Journal of Information Technology Applications and Management
• /
• 제28권1호
• /
• pp.95-110
• /
• 2021

This study aims to explore the Internet personal broadcasting quality factors that influence viewer satisfaction and dissatisfaction based on the motivation-hygiene theory. Specifically, the quality factors that affect viewer satisfaction of Internet personal broadcasting are derived from the perspectives of extrinsic (contents usefulness and media usability), intrinsic (emotional/cognitive/behavioral enjoyment and creator characteristics), and social motivation (visibility, subjective norm, image, sociality). The data of 200 respondents was used to analyze the relative impact of satisfaction and dissatisfaction with the Kano model, which assumes that viewer satisfaction at both functional and emotional levels varies over quality attributes. In the empirical analysis, the quality factors were classified into attractive, one-dimensional, must-be, and indifferent quality. In addition, it was found that the customer satisfaction coefficient was high in the order of uniqueness, differentiation, and visibility. On the other hand, as a result of applying the dissatisfaction coefficient, it was identified in the order of donation, content reliability, and creator responsiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권4호
• /
• pp.1441-1456
• /
• 2015

The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing,. The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권12호
• /
• pp.5955-5977
• /
• 2018

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

• 한국통신학회논문지
• /
• 제29권2C호
• /
• pp.229-237
• /
• 2004

본 논문은 카메라자동교정(autocalibration) 방법에서의 새로운 알고리즘을 제안한다. 3차원 기반의 영상합성은 크게 두 분야로 나뉘어 진다. 하나는 본 논문에서 설명하는 카메라자동교정 방법이고 다른 하나는 패턴과 같은 3차원 실측 데이터를 이용하는 방법이다. 전자의 방법은 대상 영상에 대한 제약조건이 전혀 없기 때문에 후자보다 진보된 방법이라 할 수 있다. 따라서 카메라자동교정 방법은 비선형 방정식을 유도하는 등의 복잡한 계산 과정을 거치게 된다. 이와 같은 이유로 최근에는 카메라의 내부 파라미터에 제약조건을 줌으로써 복잡한 비선형 방정식 대신에 선형방정식을 유도하는 방법이 많이 사용되고 있고 가장 대표적인 경우가 카메라의 주점(principal point)을 고정시키는 방법이다. 하지만 이렇게 카메라의 내부 파라미터에 강한 제약조건을 주는 것은 오차를 유발하는 원인이 된다. 따라서 본 논문에서는 이러한 문제점들을 해결하기 위해서 본 논문에서는 카메라자동교정에서의 새로운 알고리즘을 제안한다. 본 논문에서는 카메라의 주점을 가변적으로 적용하여 결과적으로 최적화된 카메라의 내부파라미터를 찾아내게 된다.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2005년도 추계 종합학술대회 논문집
• /
• pp.475-479
• /
• 2005

디지털 방식이 가져온 변화는 기존사회 가치체계 자체의 변화를 추구하며 남과는 다르게 차별화 시키고자하는 사고방식이 그전보다 훨씬 강해지고 있다. 따라서 변화된 환경에 맞는 다양하고 독창적인 이미지 연출이 필요하며, 이미지 제작을 위한 표현 방법이 필요하다. 본 논고는 디지털 이미지 크리에이팅에 관한 것으로 먼저 개발 대상을 선정하고, 감성차원을 통해 개발대상에서 연상되는 감성어휘를 추출한다. 이 후 대상간의 연계성을 시네틱스의 상징적 유추를 통해 시각화시키는 프로세스로 진행된다. 궁극적인 연구 목적은 단순한 이미지의 왜곡이나 변형이 아니라 본질적인 의미가 담긴 디지털 이미지 제작의 방법 모색과 프로세스 접근에 대한 태도의 전환을 통해 독창적이고 다양한 디지털 이미지 연출에 도움이 되고자 함이다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권5호
• /
• pp.2607-2627
• /
• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• 대한원격탐사학회지
• /
• 제26권6호
• /
• pp.629-644
• /
• 2010

A real-time image georeferencing system requires all inputs to be determined in real-time. The intrinsic camera parameters can be identified in advance from a camera calibration process while other control information can be derived instantaneously from real-time GPS/INS data. The bottleneck process is tie point acquisition since manual operations will be definitely obstacles for real-time system while the existing extraction methods are not fast enough. In this paper, we present a fast-and-automated image matching technique based on the KLT tracker to obtain a set of tie-points in real-time. The proposed work accelerates the KLT tracker by supplying the initial guessed tie-points computed using the GPS/INS data. Originally, the KLT only works effectively when the displacement between tie-points is small. To drive an automated solution, this paper suggests an appropriate number of depth levels for multi-resolution tracking under large displacement using the knowledge of uncertainties the GPS/INS data measurements. The experimental results show that our suggested depth levels is promising and the proposed work can obtain tie-points faster than the ordinary KLT by 13% with no less accuracy. This promising result suggests that our proposed algorithm can be effectively integrated into the real-time image georeferencing for further developing a real-time surveillance application.

• 한국소성가공학회:학술대회논문집
• /
• 한국소성가공학회 2000년도 춘계학술대회논문집
• /
• pp.129-133
• /
• 2000

It is reasonable to use the stereo vision and image processing technique to digitize 3D coordinates of grid points and to evaluate surface strains on a sheet metal parts. However this method has its intrinsic problems such as the difficulty in enhancement of bad images inevitable error due to digital image resolution of camera and frame grabber unreliability of strains and thickness evaluated from coarse grid on the corner area with large curvature and the limitation of the area that can be measured at a time. Therefore it is still hard to measure strain distribution over the entire surface of a medium,- or large-sized stamped part at a time even by using an automated strain measurement system. In this study the curvature correction algorithm based on the grid refinement and the geometry assembling algorithm based on the global error minimization (GEM) scheme are suggested. Several applications are presented to show the reliability and efficiency of these algorithms.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 하계학술대회 논문집 B
• /
• pp.883-885
• /
• 1995

Edges are one of the most important features used in various computer vision applications. Most of the known edge detection techniques are categorized into three gropus: First two approaches are to find gray level changes using first-order or second-order differentiation. The third method uses intrinsic propoeties of edges such as the result shown during scale space filtering. In this paper, we study various kind of edge detection techniques. Two images (Lenna image and a certain image which is composed of step, ramp, roof, and other artificial edge patterns) are used to compare different edge detection techniques and to verify the advantages and disadvantage of each techniques.

• 한국측량학회지
• /
• 제40권2호
• /
• pp.91-108
• /
• 2022

대부분의 경우 광학 RGB 영상을 딥러닝(DL: Deep learning)의 학습 데이터로 사용하여 객체탐지, 인식, 식별, 분류, 의미적 분할 및 객체 분할 등을 수행하지만, 실세계의 3차원 객체들을 2차원 영상으로 완전하게 파악하는 것은 한계가 있다. 그러므로 대표적인 3차원 지형 공간정보인 수치표면모델(DSM: Digital Surface Model)과 더불어 DSM에 내재된 특성정보를 이용하여 3차원 지형지물을 분석하는 것이 효과적이다. 건물과 같이 기하학적으로 정형화된 형태의 인공구조물은 3차원 공간데이터로부터 얻을 수 있는 기하학적 요소와 특성을 이용하여 객체의 분류와 형상 묘사가 가능하다. 이 연구는 고차원 시각정보(high-level visual information) 시스템에서 중요한 역할을 하는 내재된 고유의 특성정보(intrinsic information)를 기반으로 하며, 이를 위하여 객체의 기하학적 요소인 경사와 주향을 DSM으로부터 도출하고, 다방향에서 생성한 음영기복영상(SRI: Shaded Relief Image)과 함께 DL 모델의 학습 수행에 사용하였다. 실험은 ISPRS (International Society for Photogrammetry and Remote Sensing)에서 제공하는 데이터 셋 중에서 DSM과 레이블 데이터를 객체의 의미적 분류를 위해 개발된 합성곱 기반의 SegNet 학습에 사용하였다. 지형지물을 분류하고 분류 결과를 이용하여 건물을 추출하였다. 특히 DL 모델의 학습 성능 향상을 위해 학습 데이터의 여러 조합에 따른 시너지 효과를 분석하는 것에 핵심이다. 제안한 방법은 건물 분류와 추출에 효과적임을 보여주고 있다.